Generally, many studies on detecting and tracking a moving object using a camera with a fixed position and a fixed view point have been performed. A background image is obtained using the camera in the absence of a moving object, and then two consecutive images among an image sequence obtained with a predetermined interval are compared. In this case, except for a moving object, the other image areas remain the same. Therefore, when there is a moving object, it may be easy to detect the moving object by obtaining a difference between two consecutive images.
Also, many studies on detecting and tracking a moving object using a camera with a fixed position and a variable view point have been performed. When the camera moves, the background area changes and it is more difficult to detect a moving object in the camera's field of view. In this case, the current image is transformed using camera movement information extracted from two consecutive images, and then the moving object is extracted using a difference of two consecutive images among an image sequence. Also, the images of the peripheral areas to which the camera can move are previously obtained, and then the moving object is detected by comparing image features between the present image which the camera detects and the background image.
In a dynamic camera environment wherein the camera is mounted to a moving object, such as a mobile robot, (hereinafter called a mobile camera), sway caused by the flexible viewpoint variance and movement of the robot are included in the images captured by the camera. When a moving object is to be detected using a mobile dynamic camera, it is difficult to extract only the moving object by removing the camera movement information because the camera movement information is combined with the movement information of the moving object.
In order to extract the camera movement information, feature points of two consecutive images are extracted from corner points and then the extracted corner points are matched with each other. In addition, camera movement is transformed using a linear conversion or non-linear conversion method, and then the moving object is extracted using the difference of the two consecutive images. However, camera movement information cannot be exactly extracted in this manner and so the finally extracted moving object information generally has camera movement information mixed therewith.
Moving object prediction models, such as a kalman filter or a particle filter can fail to detect and track a moving object in a dynamic camera environment. It can also take a long processing time to remove the camera movement information so that it is impossible to track and extract the same in real-time.